Applications of Symmetry/Asymmetry in Data Mining and Machine Learning
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "A: Computer Science".
Deadline for manuscript submissions: 30 April 2027 | Viewed by 207
Editors
Interests: machine learning; computational intelligence; computer vision; NLP
Special Issues, Collections and Topics in MDPI journals
Interests: trustworthy machine learning; trustworthy machine reasoning; federated learning; out-of-distribution learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Symmetry and asymmetry are fundamental patterns in data, models, and intelligent behavior. In data mining and machine learning, they appear in feature distributions, graph structures, temporal dynamics, class imbalance, causal relations, human–agent interaction, and the evolving memories of autonomous agents. This Special Issue invites original research and review articles on applications based on symmetry/asymmetry in data mining and machine learning, with particular interest in agent systems and agent memory. Relevant topics include symmetry-aware representation learning, asymmetric similarity and retrieval, imbalanced and long-tailed data mining, graph and network learning, anomaly detection, contrastive learning, continual learning, explainable decision-making, and memory-augmented agents that consolidate, retrieve, revise, or forget knowledge over time. We also welcome studies on multi-agent systems, personalized agents, trustworthy AI, and real-world applications where symmetric or asymmetric structures improve robustness, interpretability, adaptation, and decision support. Contributions may present new algorithms, benchmark datasets, evaluation protocols, theoretical analysis, or deployed systems in domains such as scientific discovery, healthcare, education, finance, disaster intelligence, software engineering, and industrial automation. This Special Issue aims to connect mathematical structure with practical intelligent systems, showing how symmetry and asymmetry can guide more reliable, adaptive, and human-aligned machine learning applications.
Prof. Dr. Hang Yu
Dr. Yonggang Zhang
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- symmetry and asymmetry
- data mining
- machine learning
- agent memory
- autonomous agents
- representation learning
- imbalanced data
- graph learning
- explainable AI
- trustworthy AI
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